Despite superficial similarities, data-enabled learning does not necessarily create network effects, and when it does, data network effects are usually weaker and less conducive to lock-in than standard network effects. In this article, Andrei Hagiu, Associate Professor of Information Systems at Boston University, and Julian Wright, Oxera Associate and Professor of Economics at the National University of Singapore, consider how policies that aim to correct market inefficiencies associated with data-enabled learning (such as mandatory data-sharing by incumbents or data privacy restrictions) have unintended consequences that may end up hurting customers overall
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